PESTO: Parameter EStimation TOolbox

Stapor, Paul; Weindl, Daniel; Ballnus, Benjamin; Hug, Sabine; Loos, Carolin; Fiedler, Anna; Krause, Sabrina; Hroß, Sabrina; Fröhlich, Fabian; Hasenauer, Jan

doi:10.1093/bioinformatics/btx676

Cited by 102 publications

(100 citation statements)

References 11 publications

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“…We use the PESTO toolbox (Stapor et al, 2018) to maximize the likelihood including uncertainty estimation via posterior sampling as can be seen exemplarily in Figure 3B for hemispheres with ∆t=48h and for all hemispheres in Supplementary Figure 3.…”

Section: Influence Modelmentioning

confidence: 99%

Aggregated spatio-temporal division patterns emerge from reoccurring divisions of neural stem cells

Lupperger

Marr

Chapouton

2020

Preprint

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The regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms however are poorly understood. In particular it is unclear how the activity of single stem cells is coordinated within the population, or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brains revealed weakly aggregated, non-random division patterns in space and time. Analyzing divisions at two timepoints allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle re-entries in roughly 15% of newly born NSCs. In agent based simulations of NSC populations, this re-dividing activity sufficed to induce aggregated spatio-temporal division patterns that matched the ones observed experimentally. In contrast, omitting re-divisions lead to a random spatio-temporal distribution of dividing cells. Spatio-temporal aggregation of dividing stem cells can thus emerge from the cell's history, regardless of possible feedback mechanisms in the population. Lupperger et al. draft 2020Main text

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Section: Influence Modelmentioning

confidence: 99%

Aggregated spatio-temporal division patterns emerge from reoccurring divisions of neural stem cells

Lupperger

Marr

Chapouton

2020

Preprint

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“…The MATLAB toolbox PESTO [29] was used to determine parameter confidence intervals based on profile likelihoods calculated by Eq. (7).…”

Section: Parameters and Parameter Correlationsmentioning

confidence: 99%

Modeling and Parameter Identification for a Biofilm in a Microbial Fuel Cell

Kubannek

Krewer

2019

Chemie Ingenieur Technik

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A dynamic physical model of a microbial fuel cell (MFC) anode is presented and parameterized. It describes the evolution of current density and biofilm mass over time as a function of substrate concentration. The model is particularly useful for process monitoring or control purposes because it reproduces the dynamics of the MFC anode and contains comparatively few parameters. Parameters are identified using data from the response of the MFC to a substrate concentration pulse. Theoretical and practical identifiability of the parameters is evaluated, and parameter confidence intervals are determined.

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“…As disease maps usually possess hundreds or even thousands of state variables and parameters, the resulting computational complexity might be challenging for established toolboxes such as COmplex PAthway SImulator (COPASI) [58], Data2Dynamics [59], Parameter EStimation TOolbox (PESTO) [60] or PottersWheel [61]. Moreover, such a large number of variables will require an automated procedure to check parameter identifiability.…”

Section: Parameterization or Executable Mathematical Modelsmentioning

confidence: 99%

Community-driven roadmap for integrated disease maps

Ostaszewski¹,

Gebel²,

Kuperstein³

et al. 2018

Preprint

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The Disease Maps Project builds upon a network of scientific and clinical groups that exchange best practices, share information, and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation, or serve as a scaffold for the generation of predictive mathematical models.This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and web-tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations in order to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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PESTO: Parameter EStimation TOolbox

Cited by 102 publications

References 11 publications

Aggregated spatio-temporal division patterns emerge from reoccurring divisions of neural stem cells

Aggregated spatio-temporal division patterns emerge from reoccurring divisions of neural stem cells

Modeling and Parameter Identification for a Biofilm in a Microbial Fuel Cell

Community-driven roadmap for integrated disease maps

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